The present invention relates generally to motors and, more particularly, to a system and method for proactive detection of conditions indicative of potential motor faults. Baseline data is generated by a wellness relay operating in a “learning” mode by monitoring a given motor known to be operating under “healthy” conditions. After the “learning” mode is complete, the wellness relay monitors the given motor and performs at least one of current signature analysis (CSA) and power signature analysis (PSA) to determine a motor fault index of the given motor. Specifically, frequency spectrum components within carefully selected sidebands are summed and mapped to one of a plurality of load bins. By comparing the motor fault index to the baseline data associated with the mapped load bin, the wellness relay detects conditions indicative of potential motor faults and communicates wellness alerts prior to an occurrence of a potential motor fault.
In North America, three-phase induction motors consume a large percentage of all generated electrical capacity. Many applications for this “workhorse” of industry are fan and pump industrial applications. For example, in a typical integrated paper mill, low voltage and medium voltage motors may comprise nearly 70% of all driven electrical loads. Due to the prevalence of these motors in industry, it is paramount that the three-phase motor be reliable. Industry reliability surveys suggest that motor failures typically fall into one of four major categories. Specifically, motor faults typically result from bearing failure, stator turn faults, rotor bar failure, or other faults/failures. Within these four categories: bearing, stator, and rotor failure account for approximately 85% of all motor failures.
This percentage could be significantly reduced if the driven equipment was properly aligned when installed, and remained aligned regardless of changes in operating conditions. However, motors are often coupled to misaligned pump loads or loads with rotational unbalance and fail prematurely due to stresses imparted upon the motor bearings. Furthermore, manually detecting such fault causing conditions is difficult at best because doing so requires the motor to be running. As such, an operator is usually required to remove the motor from operation to perform a maintenance review and diagnosis. However, removing the motor from service is unsuitable in many industries because motor down-time is extremely costly and undesirable in many applications.
As such, some detection devices have been designed that generate feedback regarding an operating motor. The feedback is then reviewed by an operator to determine the operating conditions of the motor. However, most systems that monitor operating motors merely provide feedback of faults that have already damaged the motor. As such, though operational feedback is sent to the operator, it is usually too late for preventive action to be taken.
Some systems have attempted to provide an operator with early fault warning feedback. For example, vibration monitoring has been utilized to provide some early misalignment or unbalance based faults. However, when a mechanical resonance occurs, machine vibrations are amplified. Due to this amplification, false positives indicating severe mechanical asymmetry are possible. Furthermore, vibration based monitoring systems typically require highly invasive and specialized monitoring systems to be deployed within the motor system
As such, other systems perform some signature analysis on feedback from the motor and attempt to detect deviations indicative of a fault. While these systems may aid the operator in maintenance reviews of an operating motor, they are typically invasive and require highly specialized sensors to monitor a specific motor application. That is, the detection devices are generally an autonomous unit with sensors that must be deployed around or within the motor. Therefore, the detection devices constitute another system that must be invasively deployed within the motor system and which is susceptible to faults and deterioration. Additionally, connecting these specialized sensors usually requires specialized tools, protective devices and/or clothing and highly skilled technicians because these sensors are intended to be deployed to energized parts. Accordingly, while traditional monitoring devices allow the operator to safely receive feedback regarding an operating motor, the devices present additional autonomous systems associated with the motor which must be set-up, monitored, and maintained. Therefore, traditional motor monitoring devices compound the cost of operating the motors.
Furthermore, such early fault warning feedback systems typically require multiple levels of configuration and tailoring to properly monitor a particular motor and that motor within a particular application. That is, such systems must be individually configured to a specific motor, load, and application. For example, applications such as motor driven fans and pumps are typically constant load applications. On the other hand, applications such as conveyers or material handling applications are typically varying load applications. Generally, traditional early fault warning feedback systems must be manually calibrated not only for the individual motor but also for the specific application within which the motor is operating. Therefore, traditional early fault warning feedback systems require considerable investments in time and engineering to deploy the system.
Additionally, these systems must be regularly recalibrated to reconfigure the system for normal operational changes to the motor, load, and/or application, else risk false positives or negatives arising from normal changes to the motor signature used for review. Such recalibrations must adjust for new load variances, changes to the motor-load configuration, changes in operational frequency, and new application variances, to name but a few. Therefore, while these early fault warning feedback systems may be capable of alerting an operator of required maintenance, the systems alone may require maintenance and corresponding downtime exceeding that of the monitored motor.
It would therefore be desirable to design a system and method to non-invasively perform diagnostics on an operating motor that is specific to that motor. Additionally, it would be desirable for the system and method to be implementable utilizing traditional motor systems in order to avoid introducing additional autonomous sub-systems. Furthermore, it would be desirable that the system and method be capable of proactively diagnosing conditions indicative of a wide range of potential faults including mechanical faults and cavitation faults and be able to alert an operator of an impending fault prior to an actual fault occurrence. Also, it would be advantageous that the system and method be capable of adjusting to a wide variety of motors, loads, motor signatures, and applications and be capable of dynamically adjusting to normal changes in the system over time.